1  Introduction to R and R Studio

1.0.1 Overview of RStudio.cloud (Now called Posit Cloud)

1.1 By the end of this section, you should be able to:

• Download R and R studio
• Identify the R script, R console, Data environment and file browser in R studio
• Write and run R code from a script
• Install and load R packages

1.2 Why learn / use R?

1.2.1 Some information about R

• R is developed and used by scientists and researchers around the world
• Open source = no cost
• Constant development
• Connects to other data science/research tools
• Worldwide community: training widely available
• Encourages transparency and reproducibility
• Publication-ready outputs

1.2.2 Moving from other software to R

• Workflow is different
  • Organise files and data differently
  • Workspace can contain data and outputs
  • Can manage multiple datasets within a workspace
• Learning curve can be steep initially
  • e.g. Variables and coding, scripts
• Need to know what you want
  • e.g. building your regression model / ANOVA error terms

1.3 R has many advantages

• Using scripts means analysis is easy to follow and reproduce
• R scripts are small, online collaboration, no SPSS “older version” problems
• Data can be organised and reorganised however you need it (tidyr)
• Packages are available for “cutting edge” analysis: e.g. Big Data & Machine Learning
• A robust language for precise plots and graphics (ggplot)
• R analysis code can be embdeded into documents and presentations (R Markdown)

1.4 Download R and R Studio

Click on these links to download:

• R project
• RStudio

1.5 The R Studio environment

The interface for R Studio looks daunting at first. However, there are 4 main sections, 2 on the left and 2 on the right.

• MAIN TOP: R Script files or R Document Files
  • Where we usually type our code as a script before we run it. Script files are usually saved so we can work on them and rerun the code again later (.R files).
• MAIN BOTTOM: Console
  • Shows the output of our R code. We can type R code directly into the console and the answer will ouput immediately. However, it is more convenient to use script files.
• RIGHT TOP: Environment
  • Contains all of the objects (e.g. data, analysis, equations, plots) that are currently stored in memory. We can save all of this to a file and load it later (.RData files).
• RIGHT BOTTOM: File Browser
  • The folder that R is working from is called ‘the working directory’ and it will automatically look for files there if we try to import something (e.g. a data file). Using the more button on the file browser allows you to set your desired working directory.

1.6 Working with a script

Scripts can be opened from the File > New File > R Script menu.

Creating a new script

The purpose of scripts is to allow you to type your analysis code and save it for use later. Scripts include, for example:

• Code for importing data into R
• Your analysis code (e.g. t-test or descriptive statistics)
• Code for graphs and tables
• Comments and notes (preceded by the ‘#’ symbol)

Example of an R script

To run a script, you click the Run button. You can choose to:

• Run the whole script
• Run the selected line of code

The run button

When you run the script, you will normally see output in the console.

Output appears in the console

If your script contains code for a plot (graph), it will appear in the Plots window in the bottom right.

Plots appear in the plot window

1.7 Installing and loading packages

install Packages from RStudio, Inc. on Vimeo.

Packages add functionality to R and allow us to do new types of analysis.

• They can be installed via the menu Tools -> Install Packages
• The can also be installed using code:

install.packages()

For example, tidyr is a package that contains functions for sorting and organising data. To install the package:

Installing a package in RStudio

or use the code:

install.packages(“tidyr”)

Once a package is has been installed, you need tp load it using the library() command. For example:

   library(tidyr)

Revision Quiz

What is the purpose of R scripts in the context of data analysis? To create interactive visualizations To organize files and folders on your computer To type and save analysis code for later use To display the output of R code immediately

What does the Environment in R Studio primarily display? The R script files Publication-ready outputs Objects (data, analysis, plots) currently in memory The R package installation menu

Which of the following is a reason to learn and use R? It is a closed-source software It is used exclusively by programmers It has a large number of packages and a wide community of users It doesn’t require any coding skills

How can you install an R package in R Studio? By clicking ‘Load’ in the Environment section By running the package’s executable file By using the ‘install.packages()’ function By copying the package files to the working directory

What does the ‘Run’ button do in R Studio? It compiles the R script into a standalone program It generates publication-ready outputs It runs the code in the R script It opens the R package manager

How can you load a previously installed R package? By clicking the ‘Load’ button in the Console By typing ‘install.packages()’ in the Console By using the ‘library(package_name)’ command By copying the package files to the working directory

True or False: You can work on multiple datasets at the same time using R / R Studio? TRUE FALSE

What advantage does using R scripts for analysis provide? They enable real-time collaboration with other users They allow you to create complex interactive visualizations They encourage transparency and reproducibility of analysis They automatically generate publication-ready reports

What is the main role of the R Console in R Studio? To create interactive visualizations To dispay objects in memory To organize and manage files in the workspace To provide an interactive environment to run R code

“What is the difference between R and R Studio? R is a statistical programming language, while R Studio is a software developed for using R to do analysis R is a data visualization tool, while R Studio is a statistical analysis software R is used for web development, while R Studio is used for machine learning R is the name of the software, while R Studio is a community forum”